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Recent Advances in Spectral Analysis Techniques for Non-Destructive Detection of Internal Quality in Watermelon and Muskmelon: A Review |
MA Ben-xue1,2*, YU Guo-wei1,2, WANG Wen-xia1,2, LUO Xiu-zhi1,2, LI Yu-jie1,2, LI Xiao-zhan1,2, LEI Sheng-yuan1,2 |
1. College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
2. Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture, Shihezi 832003, China |
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Abstract Watermelon and muskmelon are sweet, juicy and rich in nutrients.There is great significance in manufacture and circulation for its internal quality detection. The traditional detection methods for internal quality of watermelon and muskmelon are inefficient, long time, high cost and destructive, which can not meet the needs of modern production. With the rapid development of spectral analysis techniques, near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) for the internal quality non-destructive detectionof watermelon and muskmelon has become a research hotspot. In order to track national and international progress of research, this paper presents the technical characteristics and system composition of NIRS and HIS. The spectral information analysis methods are concluded, including spectral information preprocessing, variable selection, model establishment and evaluation. Afterwards, the recent progress of NIRS and HSI in the non-destructive detection for the internal quality (soluble solids content, firmness, total acid content, maturity and moisture, etc.) of watermelon and muskmelon is summarized. Finally, the future trends of spectral analysis techniques in the internal qualitynon-destructive detection of watermelon and muskmelon are discussed from the technical difficulties and practical applications.This review indicates thatthe following aspects are identified as the direction of future research, using deep learning methods to analyze spectral information, establishing comprehensive evaluation model of multi-feature information fusion, and developing the rapid non-destructive detection system based on the deep integration of artificial intelligence and mobile terminal.
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Received: 2019-06-10
Accepted: 2019-10-21
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Corresponding Authors:
MA Ben-xue
E-mail: mbx_shz@163.com
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[1] Ministry of Agriculture of the People’s Republic of China(中华人民共和国农业部). NY/T 427—2016 Green Food—Watermelon and Muskmelon(NY/T 427—2016 绿色食品西甜瓜), 2016.
[2] Tian H Q, Wang C G, Zhang H J, et al. Sensor Letters,2012, 10(1): 570.
[3] Wang A C, Fu X P, Xie L J. Food Analytical Methods,2014, 8(6): 1403.
[4] Wang H L, Peng J Y, Xie C Q, et al. Sensors,2015, 15(5): 11889.
[5] ElMasry G M, Nakauchi S. Biosystems Engineering,2016, 142: 53.
[6] Hussain A, Pu H B, Sun D W. Trends in Food Science & Technology,2018, 72: 144.
[7] Sun J T, Ma B X, Dong J, et al. Journal of Food Process Engineering,2017, 40(3): e12496.
[8] Arendse E, Fawole O A, Magwaza L S, et al. Journal of Food Engineering,2018, 5(10): 22481.
[9] Porep J U, Kammerer D R, Carle R. Trends in Food Science & Technology,2015, 46(2): 211.
[10] Li J L, Sun D W, Cheng J H. Comprehensive Reviews in Food Science and Food Safety,2016, 15(5): 897.
[11] PENG Yan-kun(彭彦昆). Nondestructive Optical Technology for Agro-Food Quality and Safety Assessment(农畜产品品质安全光学无损快速检测技术). Beijing: Science Press (北京:科学出版社), 2016. 2.
[12] Wang H L, Peng J Y, Xie C Q, et al. Sensors,2015, 15(5): 11889.
[13] Yun Y H, Li H D, Deng B C, et al. Trends in Analytical Chemistry,2019, 113: 102.
[14] LIU Jun, WU Meng-ting, TAN Zheng-lin, et al(刘 军,吴梦婷,谭正林,等). Journal of Wuhan Institute of Technology(武汉工程大学学报),2017, 39(5): 496.
[15] Sirisomboon P. Materials Today: Proceedings,2018, 5(10): 22481.
[16] CHU Xiao-li(褚小立). Practical Manual for Near Infrared Spectroscopy(近红外光谱分析技术实用手册). Beijing: China Machine Press(北京:机械工业出版社), 2016.
[17] Jie D F, Xie L J, Fu X P, et al. Journal of Food Engineering,2013, 118(4): 387.
[18] JIE Deng-fei, XIE Li-juan, RAO Xiu-qin, et al(介邓飞,谢丽娟,饶秀勤,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2013, 29(12): 264.
[19] JIE Deng-fei, CHEN Meng, XIE Li-juan, et al(介邓飞,陈 猛,谢丽娟,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2014, 30(9): 229.
[20] Jie D F, Xie L J, Rao X Q, et al. Postharvest Biology and Technology,2014,90: 1.
[21] Qi S Y, Song S H, Jiang S N, et al. Journal of Innovative Optical Health Sciences,2014, 7(4): 1350034.
[22] QIAN Man, HUANG Wen-qian, WANG Qing-yan, et al(钱 曼, 黄文倩, 王庆艳, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2016, 36(6): 1700.
[23] Tamburini E, Costa S, Rugiero I, et al. Sensors,2017, 17(4): 746.
[24] Jie D F, Zhou W H, Wei X. Scientia Horticulturae,2019, 257:108718.
[25] Lu J, Qi S Y, Liu R, et al. Journal of Innovative Optical Health Sciences,2015, 8(6): 1550032.
[26] Zhang D Y, Xu L, Wang Q Y, et al. Food Analytical Methods,2019, 12(1): 136.
[27] Jie D F, Wei X. Computers and Electronics in Agriculture,2018, 151: 156.
[28] LI Feng-xia, MA Ben-xue, HE Qing-hai, et al(李锋霞,马本学,何青海,等). Acta Photonica Sinica(光子学报),2013, 42(5): 592.
[29] SUN Jing-tao, MA Ben-xue, DONG Juan, et al(孙静涛,马本学,董 娟,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2017, 37(7): 2184.
[30] Sun M J, Zhang D, Liu L, et al. Food Chemistry,2017, 218: 413. |
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